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-rw-r--r--extensions-builtin/Lora/extra_networks_lora.py10
-rw-r--r--extensions-builtin/Lora/network.py7
-rw-r--r--extensions-builtin/Lora/network_norm.py28
-rw-r--r--extensions-builtin/Lora/networks.py141
-rw-r--r--extensions-builtin/Lora/scripts/lora_script.py25
-rw-r--r--extensions-builtin/Lora/ui_edit_user_metadata.py2
-rw-r--r--extensions-builtin/Lora/ui_extra_networks_lora.py3
-rw-r--r--extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js56
-rw-r--r--extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py1
-rw-r--r--extensions-builtin/extra-options-section/scripts/extra_options_section.py44
10 files changed, 265 insertions, 52 deletions
diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py
index ba2945c6..005ff32c 100644
--- a/extensions-builtin/Lora/extra_networks_lora.py
+++ b/extensions-builtin/Lora/extra_networks_lora.py
@@ -6,9 +6,14 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
def __init__(self):
super().__init__('lora')
+ self.errors = {}
+ """mapping of network names to the number of errors the network had during operation"""
+
def activate(self, p, params_list):
additional = shared.opts.sd_lora
+ self.errors.clear()
+
if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
@@ -56,4 +61,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)
def deactivate(self, p):
- pass
+ if self.errors:
+ p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
+
+ self.errors.clear()
diff --git a/extensions-builtin/Lora/network.py b/extensions-builtin/Lora/network.py
index 0a18d69e..d8e8dfb7 100644
--- a/extensions-builtin/Lora/network.py
+++ b/extensions-builtin/Lora/network.py
@@ -133,7 +133,7 @@ class NetworkModule:
return 1.0
- def finalize_updown(self, updown, orig_weight, output_shape):
+ def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
if self.bias is not None:
updown = updown.reshape(self.bias.shape)
updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
@@ -145,7 +145,10 @@ class NetworkModule:
if orig_weight.size().numel() == updown.size().numel():
updown = updown.reshape(orig_weight.shape)
- return updown * self.calc_scale() * self.multiplier()
+ if ex_bias is not None:
+ ex_bias = ex_bias * self.multiplier()
+
+ return updown * self.calc_scale() * self.multiplier(), ex_bias
def calc_updown(self, target):
raise NotImplementedError()
diff --git a/extensions-builtin/Lora/network_norm.py b/extensions-builtin/Lora/network_norm.py
new file mode 100644
index 00000000..ce450158
--- /dev/null
+++ b/extensions-builtin/Lora/network_norm.py
@@ -0,0 +1,28 @@
+import network
+
+
+class ModuleTypeNorm(network.ModuleType):
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
+ if all(x in weights.w for x in ["w_norm", "b_norm"]):
+ return NetworkModuleNorm(net, weights)
+
+ return None
+
+
+class NetworkModuleNorm(network.NetworkModule):
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
+ super().__init__(net, weights)
+
+ self.w_norm = weights.w.get("w_norm")
+ self.b_norm = weights.w.get("b_norm")
+
+ def calc_updown(self, orig_weight):
+ output_shape = self.w_norm.shape
+ updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+
+ if self.b_norm is not None:
+ ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+ else:
+ ex_bias = None
+
+ return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 17cbe1bb..c252ed9e 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -1,3 +1,4 @@
+import logging
import os
import re
@@ -7,6 +8,7 @@ import network_hada
import network_ia3
import network_lokr
import network_full
+import network_norm
import torch
from typing import Union
@@ -19,6 +21,7 @@ module_types = [
network_ia3.ModuleTypeIa3(),
network_lokr.ModuleTypeLokr(),
network_full.ModuleTypeFull(),
+ network_norm.ModuleTypeNorm(),
]
@@ -31,6 +34,8 @@ suffix_conversion = {
"resnets": {
"conv1": "in_layers_2",
"conv2": "out_layers_3",
+ "norm1": "in_layers_0",
+ "norm2": "out_layers_0",
"time_emb_proj": "emb_layers_1",
"conv_shortcut": "skip_connection",
}
@@ -190,11 +195,19 @@ def load_network(name, network_on_disk):
net.modules[key] = net_module
if keys_failed_to_match:
- print(f"Failed to match keys when loading network {network_on_disk.filename}: {keys_failed_to_match}")
+ logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
return net
+def purge_networks_from_memory():
+ while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0:
+ name = next(iter(networks_in_memory))
+ networks_in_memory.pop(name, None)
+
+ devices.torch_gc()
+
+
def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
already_loaded = {}
@@ -212,15 +225,19 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
failed_to_load_networks = []
- for i, name in enumerate(names):
+ for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
net = already_loaded.get(name, None)
- network_on_disk = networks_on_disk[i]
-
if network_on_disk is not None:
+ if net is None:
+ net = networks_in_memory.get(name)
+
if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
try:
net = load_network(name, network_on_disk)
+
+ networks_in_memory.pop(name, None)
+ networks_in_memory[name] = net
except Exception as e:
errors.display(e, f"loading network {network_on_disk.filename}")
continue
@@ -231,7 +248,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
if net is None:
failed_to_load_networks.append(name)
- print(f"Couldn't find network with name {name}")
+ logging.info(f"Couldn't find network with name {name}")
continue
net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
@@ -240,23 +257,30 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
loaded_networks.append(net)
if failed_to_load_networks:
- sd_hijack.model_hijack.comments.append("Failed to find networks: " + ", ".join(failed_to_load_networks))
+ sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
+ purge_networks_from_memory()
-def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+
+def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
weights_backup = getattr(self, "network_weights_backup", None)
+ bias_backup = getattr(self, "network_bias_backup", None)
- if weights_backup is None:
+ if weights_backup is None and bias_backup is None:
return
- if isinstance(self, torch.nn.MultiheadAttention):
- self.in_proj_weight.copy_(weights_backup[0])
- self.out_proj.weight.copy_(weights_backup[1])
- else:
- self.weight.copy_(weights_backup)
+ if weights_backup is not None:
+ if isinstance(self, torch.nn.MultiheadAttention):
+ self.in_proj_weight.copy_(weights_backup[0])
+ self.out_proj.weight.copy_(weights_backup[1])
+ else:
+ self.weight.copy_(weights_backup)
+ if bias_backup is not None:
+ self.bias.copy_(bias_backup)
-def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+
+def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
"""
Applies the currently selected set of networks to the weights of torch layer self.
If weights already have this particular set of networks applied, does nothing.
@@ -279,21 +303,33 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
self.network_weights_backup = weights_backup
+ bias_backup = getattr(self, "network_bias_backup", None)
+ if bias_backup is None and getattr(self, 'bias', None) is not None:
+ bias_backup = self.bias.to(devices.cpu, copy=True)
+ self.network_bias_backup = bias_backup
+
if current_names != wanted_names:
network_restore_weights_from_backup(self)
for net in loaded_networks:
module = net.modules.get(network_layer_name, None)
if module is not None and hasattr(self, 'weight'):
- with torch.no_grad():
- updown = module.calc_updown(self.weight)
+ try:
+ with torch.no_grad():
+ updown, ex_bias = module.calc_updown(self.weight)
- if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
- # inpainting model. zero pad updown to make channel[1] 4 to 9
- updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
+ if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+ # inpainting model. zero pad updown to make channel[1] 4 to 9
+ updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
- self.weight += updown
- continue
+ self.weight += updown
+ if ex_bias is not None and getattr(self, 'bias', None) is not None:
+ self.bias += ex_bias
+ except RuntimeError as e:
+ logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+ continue
module_q = net.modules.get(network_layer_name + "_q_proj", None)
module_k = net.modules.get(network_layer_name + "_k_proj", None)
@@ -301,21 +337,28 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
module_out = net.modules.get(network_layer_name + "_out_proj", None)
if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
- with torch.no_grad():
- updown_q = module_q.calc_updown(self.in_proj_weight)
- updown_k = module_k.calc_updown(self.in_proj_weight)
- updown_v = module_v.calc_updown(self.in_proj_weight)
- updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
- updown_out = module_out.calc_updown(self.out_proj.weight)
-
- self.in_proj_weight += updown_qkv
- self.out_proj.weight += updown_out
- continue
+ try:
+ with torch.no_grad():
+ updown_q = module_q.calc_updown(self.in_proj_weight)
+ updown_k = module_k.calc_updown(self.in_proj_weight)
+ updown_v = module_v.calc_updown(self.in_proj_weight)
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+ updown_out = module_out.calc_updown(self.out_proj.weight)
+
+ self.in_proj_weight += updown_qkv
+ self.out_proj.weight += updown_out
+
+ except RuntimeError as e:
+ logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+ continue
if module is None:
continue
- print(f'failed to calculate network weights for layer {network_layer_name}')
+ logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
self.network_current_names = wanted_names
@@ -342,7 +385,7 @@ def network_forward(module, input, original_forward):
if module is None:
continue
- y = module.forward(y, input)
+ y = module.forward(input, y)
return y
@@ -382,6 +425,36 @@ def network_Conv2d_load_state_dict(self, *args, **kwargs):
return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs)
+def network_GroupNorm_forward(self, input):
+ if shared.opts.lora_functional:
+ return network_forward(self, input, torch.nn.GroupNorm_forward_before_network)
+
+ network_apply_weights(self)
+
+ return torch.nn.GroupNorm_forward_before_network(self, input)
+
+
+def network_GroupNorm_load_state_dict(self, *args, **kwargs):
+ network_reset_cached_weight(self)
+
+ return torch.nn.GroupNorm_load_state_dict_before_network(self, *args, **kwargs)
+
+
+def network_LayerNorm_forward(self, input):
+ if shared.opts.lora_functional:
+ return network_forward(self, input, torch.nn.LayerNorm_forward_before_network)
+
+ network_apply_weights(self)
+
+ return torch.nn.LayerNorm_forward_before_network(self, input)
+
+
+def network_LayerNorm_load_state_dict(self, *args, **kwargs):
+ network_reset_cached_weight(self)
+
+ return torch.nn.LayerNorm_load_state_dict_before_network(self, *args, **kwargs)
+
+
def network_MultiheadAttention_forward(self, *args, **kwargs):
network_apply_weights(self)
@@ -458,10 +531,12 @@ def infotext_pasted(infotext, params):
if added:
params["Prompt"] += "\n" + "".join(added)
+extra_network_lora = None
available_networks = {}
available_network_aliases = {}
loaded_networks = []
+networks_in_memory = {}
available_network_hash_lookup = {}
forbidden_network_aliases = {}
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py
index cd28afc9..4c6e774a 100644
--- a/extensions-builtin/Lora/scripts/lora_script.py
+++ b/extensions-builtin/Lora/scripts/lora_script.py
@@ -23,9 +23,9 @@ def unload():
def before_ui():
ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
- extra_network = extra_networks_lora.ExtraNetworkLora()
- extra_networks.register_extra_network(extra_network)
- extra_networks.register_extra_network_alias(extra_network, "lyco")
+ networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora()
+ extra_networks.register_extra_network(networks.extra_network_lora)
+ extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco")
if not hasattr(torch.nn, 'Linear_forward_before_network'):
@@ -40,6 +40,18 @@ if not hasattr(torch.nn, 'Conv2d_forward_before_network'):
if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_network'):
torch.nn.Conv2d_load_state_dict_before_network = torch.nn.Conv2d._load_from_state_dict
+if not hasattr(torch.nn, 'GroupNorm_forward_before_network'):
+ torch.nn.GroupNorm_forward_before_network = torch.nn.GroupNorm.forward
+
+if not hasattr(torch.nn, 'GroupNorm_load_state_dict_before_network'):
+ torch.nn.GroupNorm_load_state_dict_before_network = torch.nn.GroupNorm._load_from_state_dict
+
+if not hasattr(torch.nn, 'LayerNorm_forward_before_network'):
+ torch.nn.LayerNorm_forward_before_network = torch.nn.LayerNorm.forward
+
+if not hasattr(torch.nn, 'LayerNorm_load_state_dict_before_network'):
+ torch.nn.LayerNorm_load_state_dict_before_network = torch.nn.LayerNorm._load_from_state_dict
+
if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'):
torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward
@@ -50,6 +62,10 @@ torch.nn.Linear.forward = networks.network_Linear_forward
torch.nn.Linear._load_from_state_dict = networks.network_Linear_load_state_dict
torch.nn.Conv2d.forward = networks.network_Conv2d_forward
torch.nn.Conv2d._load_from_state_dict = networks.network_Conv2d_load_state_dict
+torch.nn.GroupNorm.forward = networks.network_GroupNorm_forward
+torch.nn.GroupNorm._load_from_state_dict = networks.network_GroupNorm_load_state_dict
+torch.nn.LayerNorm.forward = networks.network_LayerNorm_forward
+torch.nn.LayerNorm._load_from_state_dict = networks.network_LayerNorm_load_state_dict
torch.nn.MultiheadAttention.forward = networks.network_MultiheadAttention_forward
torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict
@@ -65,6 +81,7 @@ shared.options_templates.update(shared.options_section(('extra_networks', "Extra
"lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
"lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
"lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
+ "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
}))
@@ -121,3 +138,5 @@ def infotext_pasted(infotext, d):
script_callbacks.on_infotext_pasted(infotext_pasted)
+
+shared.opts.onchange("lora_in_memory_limit", networks.purge_networks_from_memory)
diff --git a/extensions-builtin/Lora/ui_edit_user_metadata.py b/extensions-builtin/Lora/ui_edit_user_metadata.py
index 2ca997f7..390d9dde 100644
--- a/extensions-builtin/Lora/ui_edit_user_metadata.py
+++ b/extensions-builtin/Lora/ui_edit_user_metadata.py
@@ -167,7 +167,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
with gr.Column(scale=1, min_width=120):
- generate_random_prompt = gr.Button('Generate').style(full_width=True, size="lg")
+ generate_random_prompt = gr.Button('Generate', size="lg", scale=1)
self.edit_notes = gr.TextArea(label='Notes', lines=4)
diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py
index 3629e5c0..55409a78 100644
--- a/extensions-builtin/Lora/ui_extra_networks_lora.py
+++ b/extensions-builtin/Lora/ui_extra_networks_lora.py
@@ -25,9 +25,10 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
item = {
"name": name,
"filename": lora_on_disk.filename,
+ "shorthash": lora_on_disk.shorthash,
"preview": self.find_preview(path),
"description": self.find_description(path),
- "search_term": self.search_terms_from_path(lora_on_disk.filename),
+ "search_term": self.search_terms_from_path(lora_on_disk.filename) + " " + (lora_on_disk.hash or ""),
"local_preview": f"{path}.{shared.opts.samples_format}",
"metadata": lora_on_disk.metadata,
"sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
diff --git a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js
index 30199dcd..72c8ba87 100644
--- a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js
+++ b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js
@@ -12,6 +12,7 @@ onUiLoaded(async() => {
"Sketch": elementIDs.sketch
};
+
// Helper functions
// Get active tab
function getActiveTab(elements, all = false) {
@@ -42,6 +43,11 @@ onUiLoaded(async() => {
}
}
+ // Detect whether the element has a horizontal scroll bar
+ function hasHorizontalScrollbar(element) {
+ return element.scrollWidth > element.clientWidth;
+ }
+
// Function for defining the "Ctrl", "Shift" and "Alt" keys
function isModifierKey(event, key) {
switch (key) {
@@ -201,7 +207,8 @@ onUiLoaded(async() => {
canvas_hotkey_overlap: "KeyO",
canvas_disabled_functions: [],
canvas_show_tooltip: true,
- canvas_blur_prompt: false
+ canvas_auto_expand: true,
+ canvas_blur_prompt: false,
};
const functionMap = {
@@ -371,6 +378,11 @@ onUiLoaded(async() => {
toggleOverlap("off");
fullScreenMode = false;
+ const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']");
+ if (closeBtn) {
+ closeBtn.addEventListener("click", resetZoom);
+ }
+
if (
canvas &&
parseFloat(canvas.style.width) > 865 &&
@@ -648,8 +660,50 @@ onUiLoaded(async() => {
mouseY = e.offsetY;
}
+ // Simulation of the function to put a long image into the screen.
+ // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element.
+ // We hide the image and show it to the user when it is ready.
+
+ targetElement.isExpanded = false;
+ function autoExpand() {
+ const canvas = document.querySelector(`${elemId} canvas[key="interface"]`);
+ const isMainTab = activeElement === elementIDs.inpaint || activeElement === elementIDs.inpaintSketch || activeElement === elementIDs.sketch;
+
+ if (canvas && isMainTab) {
+ if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) {
+ targetElement.style.visibility = "hidden";
+ setTimeout(() => {
+ fitToScreen();
+ resetZoom();
+ targetElement.style.visibility = "visible";
+ targetElement.isExpanded = true;
+ }, 10);
+ }
+ }
+ }
+
targetElement.addEventListener("mousemove", getMousePosition);
+ //observers
+ // Creating an observer with a callback function to handle DOM changes
+ const observer = new MutationObserver((mutationsList, observer) => {
+ for (let mutation of mutationsList) {
+ // If the style attribute of the canvas has changed, by observation it happens only when the picture changes
+ if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&
+ mutation.target.tagName.toLowerCase() === 'canvas') {
+ targetElement.isExpanded = false;
+ setTimeout(resetZoom, 10);
+ }
+ }
+ });
+
+ // Apply auto expand if enabled
+ if (hotkeysConfig.canvas_auto_expand) {
+ targetElement.addEventListener("mousemove", autoExpand);
+ // Set up an observer to track attribute changes
+ observer.observe(targetElement, {attributes: true, childList: true, subtree: true});
+ }
+
// Handle events only inside the targetElement
let isKeyDownHandlerAttached = false;
diff --git a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
index 380176ce..2d8d2d1c 100644
--- a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
+++ b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
@@ -9,6 +9,7 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas
"canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
"canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
"canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
+ "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"),
"canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"),
"canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
}))
diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
index a05e10d8..588b64d2 100644
--- a/extensions-builtin/extra-options-section/scripts/extra_options_section.py
+++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
@@ -1,5 +1,7 @@
+import math
+
import gradio as gr
-from modules import scripts, shared, ui_components, ui_settings
+from modules import scripts, shared, ui_components, ui_settings, generation_parameters_copypaste
from modules.ui_components import FormColumn
@@ -19,18 +21,37 @@ class ExtraOptionsSection(scripts.Script):
def ui(self, is_img2img):
self.comps = []
self.setting_names = []
+ self.infotext_fields = []
+
+ mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}
with gr.Blocks() as interface:
- with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and shared.opts.extra_options else gr.Group(), gr.Row():
- for setting_name in shared.opts.extra_options:
- with FormColumn():
- comp = ui_settings.create_setting_component(setting_name)
+ with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and shared.opts.extra_options else gr.Group():
+
+ row_count = math.ceil(len(shared.opts.extra_options) / shared.opts.extra_options_cols)
+
+ for row in range(row_count):
+ with gr.Row():
+ for col in range(shared.opts.extra_options_cols):
+ index = row * shared.opts.extra_options_cols + col
+ if index >= len(shared.opts.extra_options):
+ break
+
+ setting_name = shared.opts.extra_options[index]
- self.comps.append(comp)
- self.setting_names.append(setting_name)
+ with FormColumn():
+ comp = ui_settings.create_setting_component(setting_name)
+
+ self.comps.append(comp)
+ self.setting_names.append(setting_name)
+
+ setting_infotext_name = mapping.get(setting_name)
+ if setting_infotext_name is not None:
+ self.infotext_fields.append((comp, setting_infotext_name))
def get_settings_values():
- return [ui_settings.get_value_for_setting(key) for key in self.setting_names]
+ res = [ui_settings.get_value_for_setting(key) for key in self.setting_names]
+ return res[0] if len(res) == 1 else res
interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False)
@@ -43,6 +64,9 @@ class ExtraOptionsSection(scripts.Script):
shared.options_templates.update(shared.options_section(('ui', "User interface"), {
- "extra_options": shared.OptionInfo([], "Options in main UI", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img/img2img interfaces").needs_restart(),
- "extra_options_accordion": shared.OptionInfo(False, "Place options in main UI into an accordion")
+ "extra_options": shared.OptionInfo([], "Options in main UI", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img/img2img interfaces").needs_reload_ui(),
+ "extra_options_cols": shared.OptionInfo(1, "Options in main UI - number of columns", gr.Number, {"precision": 0}).needs_reload_ui(),
+ "extra_options_accordion": shared.OptionInfo(False, "Options in main UI - place into an accordion").needs_reload_ui()
}))
+
+
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-22 22:23:49 +0000